# Using Data Science to Understand the Film Industry's Gender Gap

arXiv: https://arxiv.org/abs/1903.06469 · HTML: https://mlsanity.com/paper/1903.06469/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues](https://mlsanity.com/paper/2205.15951/) (2022)
- [Causal effect of racial bias in data and machine learning algorithms on user persuasiveness & discriminatory decision making: An Empirical Study](https://mlsanity.com/paper/2202.00471/) (2022)
- [Identifying gender bias in blockbuster movies through the lens of machine learning](https://mlsanity.com/paper/2211.12504/) (2022)
- [Understanding Social Structures from Contemporary Literary Fiction using Character Interaction Graph -- Half Century Chronology of Influential Bengali Writers](https://mlsanity.com/paper/2310.16968/) (2023)
- [Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora](https://mlsanity.com/paper/2508.13169/) (2025)
- [You Talkin to Me?: A Network Analysis of Gendered Speaker-Addressee Patterns in Film Screenplays](https://mlsanity.com/paper/2607.22656/) (2026)

## Abstract (arXiv)

Data science can offer answers to a wide range of social science questions. Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem and career choice. To this end, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks). Analyzing this data, we investigated gender bias in on-screen female characters over the past century. We find a trend of improvement in all aspects of women`s roles in movies, including a constant rise in the centrality of female characters. There has also been an increase in the number of movies that pass the well-known Bechdel test, a popular--albeit flawed--measure of women in fiction. Here we propose a new and better alternative to this test for evaluating female roles in movies. Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies.